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== Reference ==
Johannes Fürnkranz, Eyke Hüllermeier: [[Preference Learning and Ranking by Pairwise Comparison]]. In: Fürnkranz, J. and Hüllermeier, E.: [[Preference Learning]], 2011, 65-82.

== DOI ==
http://dx.doi.org/10.1007/978-3-642-14125-6_4

== Abstract ==
This chapter provides an overview of recent work on preference learning and ranking via pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural machine learning counterpart to the relational approach to preference modeling and decision making. From a machine learning point of view, LPC is especially appealing as it decomposes a possibly complex prediction problem into a certain number of learning problems of the simplest type, namely binary classification. We explain how to approach different preference learning problems, such as label and instance ranking, within the framework of LPC. We primarily focus on methodological aspects, but also address theoretical questions as well as algorithmic and complexity issues.

== Extended Abstract ==

== Bibtex ==
@incollection{
year={2011},
isbn={978-3-642-14124-9},
booktitle={Preference Learning},
editor={Fürnkranz, Johannes and Hüllermeier, Eyke},
doi={10.1007/978-3-642-14125-6_4},
title={Preference Learning and Ranking by Pairwise Comparison},
url={http://dx.doi.org/10.1007/978-3-642-14125-6_4, http://de.evo-art.org/index.php?title=Preference_Learning_and_Ranking_by_Pairwise_Comparison },
publisher={Springer Berlin Heidelberg},
author={Fürnkranz, Johannes and Hüllermeier, Eyke},
pages={65-82},
language={English}
}

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== Links ==
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Preference Learning and Ranking by Pairwise Comparison - Versionsgeschichte

Gubachelier am 29. November 2015 um 11:24 Uhr

Gubachelier: Die Seite wurde neu angelegt: „ == Reference == Johannes Fürnkranz, Eyke Hüllermeier: Preference Learning and Ranking by Pairwise Comparison. In: Fürnkranz, J. and Hüllermeier, E.: […“